Most theoretical models of dysarthria have been developed based on research using individuals speaking English or other Indo-European languages. Studies of individuals with dysarthria speaking other languages can allow investigation into the universality of such models, and the interplay between language-specific and language-universal aspects of dysarthria. In this article, studies of Cantonese- and Mandarin-Chinese speakers with dysarthria are reviewed. The studies focused on 2 groups of speakers: those with cerebral palsy and those with Parkinson's disease. Key findings are compared with similar studies of English speakers. Since Chinese is tonal in nature, the impact of dysarthria on lexical tone has received considerable attention in the literature. The relationship between tone [which involves fundamental frequency (F(0)) control at the syllable level] and intonation (involving F(0) control at the sentential level) has received more recent attention. Many findings for Chinese speakers with dysarthria support earlier findings for English speakers, thus affirming the language-universal aspect of dysarthria. However, certain differences, which can be attributed to the distinct phonologies of Cantonese and Mandarin, highlight the language-specific aspects of the condition.
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http://dx.doi.org/10.1159/000287206 | DOI Listing |
J Speech Lang Hear Res
January 2025
Department of Communicative Disorders and Deaf Education, Utah State University, Logan.
Purpose: In effortful listening conditions, speech perception and adaptation abilities are constrained by aging and often linked to age-related hearing loss and cognitive decline. Given that older adults are frequent communication partners of individuals with dysarthria, the current study examines cognitive-linguistic and hearing predictors of dysarthric speech perception and adaptation in older listeners.
Method: Fifty-eight older adult listeners (aged 55-80 years) completed a battery of hearing and cognitive tasks administered via the National Institutes of Health Toolbox.
Diagnostics (Basel)
November 2024
College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan.
Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion.
View Article and Find Full Text PDFJASA Express Lett
December 2024
Electrical Engineering Department, Indian Institute of Science, Bengaluru, India.
We study inter-speaker acoustic differences during sustained vowel utterances at varied severities of Amyotrophic Lateral Sclerosis-induced dysarthria. Among source attributes, jitter and standard deviation of fundamental frequency exhibit enhanced inter-speaker differences among patients than healthy controls (HCs) at all severity levels. Though inter-speaker differences in vocal tract filter attributes at most severity levels are higher than those among HCs for close vowels /i/ and /u/, these are comparable with or lower than those among HCs for the relatively more open vowels /a/ and /o/.
View Article and Find Full Text PDFAm J Speech Lang Pathol
December 2024
Department of Communicative Disorders and Deaf Education, Utah State University, Logan.
Purpose: The purpose of the current study was to develop and test extensions to Autoscore, an automated approach for scoring listener transcriptions against target stimuli, for scoring the Speech Intelligibility Test (SIT), a widely used test for quantifying intelligibility in individuals with dysarthria.
Method: Three main extensions to Autoscore were created including a compound rule, a contractions rule, and a numbers rule. We used two sets of previously collected listener SIT transcripts ( = 4,642) from databases of dysarthric speakers to evaluate the accuracy of the Autoscore SIT extensions.
Am J Speech Lang Pathol
January 2025
Department of Speech & Hearing Sciences, Portland State University, OR.
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